A data-driven approach to estimating post-discovery parameters of unexplored oilfields

Abstract

Consider a typical situation where an investor is considering acquiring an unexplored oilfield. The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology, depth, depositional system, diagenetic overprint, structural compartmentalization, and trap type are available. In this situation, investors usually estimate production rates using a volumetric approach. A more accurate estimation of production rates can be obtained using analytical methods, which require additional data such as net pay, porosity, oil formation volume factor, permeability, viscosity, and pressure. We call these data post-discovery parameters because they are only available after discovery through exploration drilling. A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data. Using the Gaussian mixture model, and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established. We came up with 12 reservoir types. Subsequently, an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types. Based on k-fold cross-validation with k = 10, the accuracy of the classification model is stable with an average of 87.9%. With our approach, an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters' joint probability distribution. The investor can incorporate this information into a valuation model to calculate the production rates more accurately, estimate the oilfield's value and risk, and make an informed acquisition decision accordingly.